conftrace_

Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

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29

44

31

52

189

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352

382

457

787

816

828

1,066

1,391

1,913

3,012

3,646

4,303

4,450

5,357

6,155

5,854

1,521

2000

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Papers (including subtopics)

Learning Kernels over Strings using Gaussian Processes IJCNLP 2017 Learning Latent Space Models with Angular Constraints ICML 2017 Learning Linear Dynamical Systems via Spectral Filtering NeurIPS 2017 Learning Low-Dimensional Metrics NeurIPS 2017 Learning Mixture of Gaussians with Streaming Data NeurIPS 2017 Learning MSO-definable hypotheses on strings ALT 2017 Learning Multivariate Log-concave Distributions COLT 2017 Learning Optimal Interventions AISTATS 2017 Learning Overcomplete HMMs NeurIPS 2017 Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning JMLR 2017 Learning Populations of Parameters NeurIPS 2017 Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks ACML 2017 Learning RBM with a DC programming Approach ACML 2017 Learning ReLUs via Gradient Descent NeurIPS 2017 Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data NeurIPS 2017 Learning Stable Stochastic Nonlinear Dynamical Systems ICML 2017 Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding NeurIPS 2017 Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields AISTATS 2017 Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems COLT 2017 Learning Theory for Conditional Risk Minimization AISTATS 2017 Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network JMLR 2017 Learning to Learn without Gradient Descent by Gradient Descent ICML 2017 Learning to Reconstruct 3D Structures for Occupancy Mapping RSS 2017 Learning to select data for transfer learning with Bayesian Optimization EMNLP 2017 Learning to Skim Text ACL 2017